plasso (version 0.1.3)

predict.cv.plasso: Predict after cross-validated (Post-) Lasso

Description

Prediction for cross-validated (Post-) Lasso.

Usage

# S3 method for cv.plasso
predict(
  object,
  ...,
  newx = NULL,
  type = c("response", "coefficients"),
  s = c("optimal", "all"),
  se_rule = 0
)

Value

List object containing either fitted values or coefficients for both the Lasso and Post-Lasso models respectively.

lasso

Matrix with Lasso predictions or coefficients

plasso

Matrix with Post-Lasso predictions or coefficients

Arguments

object

Fitted cv.plasso model object

...

Pass generic predict options

newx

Matrix of new values for x at which predictions are to be made. If no value is supplied, x from fitting procedure is used. This argument is not used for type="coefficients".

type

Type of prediction required. "response" returns fitted values, "coefficients" returns beta estimates.

s

Determines whether prediction is done for all values of lambda ("all") or only for the optimal lambda ("optimal") according to the standard error-rule.

se_rule

If equal to 0, predictions from cross-validated MSE minimum (default). Negative values go in the direction of smaller models, positive values go in the direction of larger models (e.g. se_rule=-1 creates the standard 1SE rule). This argument is not used for s="all".

Examples

# load toeplitz data
data(toeplitz)
# extract target and features from data
y = as.matrix(toeplitz[,1])
X = toeplitz[,-1]
# fit cv.plasso to the data
p.cv = plasso::cv.plasso(X,y)
# predict fitted values along whole lambda sequence 
pred = predict(p.cv, s="all")
head(pred$plasso)
# predict fitted values for optimal lambda value (according to cross-validation) 
pred_optimal = predict(p.cv, s="optimal")
head(pred_optimal$plasso)
# predict fitted values for new feature set X
X_new = head(X, 10)
pred_new = predict(p.cv, newx=X_new, s="optimal")
pred_new$plasso
# get estimated coefficients along whole lambda sequence
coefs = predict(p.cv, type="coefficients", s="all")
head(coefs$plasso)
# get estimated coefficients for optimal lambda value according to 1-standard-error rule
predict(p.cv, type="coefficients", s="optimal", se_rule=-1)